Sparkco — Build DCF Models from Text | AI Excel Generator for Finance Teams

Hero / Value Proposition

Describe your assumptions in plain English; Sparkco outputs a working 3-statement model and DCF in under 10 minutes. Finance teams cut modeling time by 30–40% with automation (McKinsey, 2023). Every formula is traceable and audit-ready for faster reviews. Built for FP&A, banking, and investing teams.

(Cut our first-build time from 8 hours to 45 minutes — FP&A Manager)

• Speed: generate a DCF and 3-statement model in under 10 minutes.
• Accuracy: formula-level traceability; inputs, links, and assumptions documented.
• Audit trail: versioned change log and cell-level lineage.

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How It Works: From Plain English to Executable Excel

Sparkco converts a natural language spreadsheet request into a versioned, auditable Excel model. A 7-step pipeline parses the text, synthesizes formulas, maps cells and names, generates the workbook, and produces an audit trail—letting teams build model from text with enterprise controls.

Sparkco transforms plain English prompts into structured Excel workbooks through a deterministic pipeline that preserves auditability and separates assumptions from calculations. Below is the workflow, a detailed DCF example, and controls for error handling and versioning.

Workflow: From text to workbook (7 steps)

A reproducible pipeline converts unstructured text into a workbook and an accompanying audit package.

1. Prompt ingest and context capture: Receive plain English, project name, currency, dates, and required outputs via API/UI. Normalize units (m, k), capture horizon, and constraints.
2. NLP parsing and requirement extraction: Use spaCy for tokenization/NER/dependency parse, a domain lexicon for finance terms, and a transformer model (e.g., Llama/GPT class) for intent classification. Output a structured spec (JSON) with entities, time axis, measures, and constraints.
3. Formula synthesis and validation: Map the spec to a financial modeling DSL. Apply rule libraries for DCF patterns (FCFF, tax vectors, piecewise growth, terminal value methods). Validate dimensionality, monotonic time, and unit consistency; detect edge cases (g >= WACC).
4. Cell mapping and naming: Allocate a canonical layout: Inputs, Projections, DCF, Summary. Generate named ranges (WACC, TerminalGrowth, Years, Revenue, EBIT, TaxRate, CapexPct, NWCPct) and consistent columnar time mapping. Apply data validation and input formatting.
5. Formula emission: Compile DSL to Excel formulas (Office 365 compatible), preferring LET/LAMBDA and array formulas where available; fallback to classical cell-by-cell formulas for compatibility. Insert standard patterns: NPV/IRR, discount factors, TV, checks.
6. Workbook generation: Create .xlsx via openpyxl or XlsxWriter (Python), ClosedXML (C#), or exceljs (Node). Write names, styles, data validation, calculation order, and documentation sheet. Embed metadata (spec hash, engine version).
7. Audit, reporting, and versioning: Persist the full spec, dependency graph, and formula map. Store version IDs (Git commit or content hash), change logs, and diff reports. Export an audit report (CSV/JSON) with named range lineage and a reconciliation of inputs to outputs.

Example: DCF prompt to workbook outline

Stage-by-stage transform (DCF)

Key formulas and patterns

Below, Excel patterns use named ranges and columnar years (Years = {1..5}).

DCF components and Excel patterns

Concrete cell-level examples (Year columns C:G)

Assume Inputs!C3: StartRevenue = 50000000; Inputs!C4:G4: GrowthVec = 8% each; Inputs!C5: EBITMargin = 20%; Inputs!C6:G6: TaxRateVec = [25%,25%,27%,27%,27%]; Inputs!C7: DAPct = 3%; Inputs!C8: CapexPct = 4%; Inputs!C9:G9: NWCPctVec = [13%,12%,12%,12%,12%]; Inputs!C10: WACC = 10%; Inputs!C11: TerminalGrowth = 2.5%.

Selected formulas

Auditability and versioning

Sparkco preserves traceability from prompt to cell.

• Separation of concerns: Inputs sheet only for assumptions; Projections/DCF for calculations; Summary for outputs.
• Named range lineage: Each formula references named ranges; a lineage map lists source cells for every output.
• Versioned spec: The parsed JSON spec and formula graph are stored with content hash and engine version.
• Diff reports: Human-readable change log capturing modifications in assumptions, structure, and formulas.
• Checks and flags: A Checks sheet tests sign, horizons, and constraint violations (e.g., g < WACC, tax in 0–100%).
• Deterministic builds: Same prompt/spec yields identical workbook (same spec hash).
• Reproducible exports: Audit bundle includes spec.json, dependency-graph.json, formulas.csv, and checks.csv.

Error handling and edge cases

The engine validates assumptions before emission and adds guards to formulas.

• Non-linear growth: Accept vector or piecewise rules; engine constructs GrowthVec explicitly and validates length = horizon.
• Changing tax rates: TaxRateVec applied year-by-year in NOPAT; if missing years, last-observation-carried-forward with warning.
• Terminal growth conflicts: If TerminalGrowth >= WACC, terminal value is NA and a blocking error is logged.
• Negative or out-of-range rates: Data validation (0–100%) and Checks sheet; formulas clamp only in non-critical displays, not in core calcs.
• Date and timing mismatch: Switch between NPV/XNPV and IRR/XIRR based on provided dates; default is end-of-period annual NPV/IRR.
• Circularity detection: Graph builder rejects cycles; where iterative behavior is intentional (e.g., debt schedules), Excel iteration settings are documented and an alternative non-circular mode is offered.
• Division-by-zero and NA propagation: TV formula uses IF guards; Summary highlights NA drivers with links to source assumptions.

Key Features and Capabilities

Sparkco turns plain text into auditable, enterprise-grade Excel models. From text to spreadsheet, automatic formula generation, DCFs, 3-statement builds, pivots, sensitivity and scenarios, Sparkco maps every feature to a finance-ready outcome with measurable speed and accuracy.

Sparkco’s text to spreadsheet engine, automatic formula generation, and model governance tools accelerate FP&A workflows while preserving native Excel. Compared to tools like Causal, Anaplan, and DataRails, Sparkco emphasizes instant model creation in familiar spreadsheets with explainable formulas, named ranges, and built-in auditability.

Feature-to-Benefit Mapping

Text-to-spreadsheet conversion

• What: Turn plain English into a multi-sheet Excel model with time series, styles, and checks.
• How: NLP-to-schema parser maps entities (drivers, measures, time grains) to a typed workbook and writes native .xlsx.
• Benefit: Go from idea to working model in minutes without starting from a blank sheet.
• Example: IN: Create monthly revenue by product A/B for 2023–2026 with 40% COGS and gross margin. OUT: Workbook with Assumptions, Revenue, COGS, Gross Margin filled by month.
• Benchmark: ~2 minutes to first draft vs ~20 minutes manual; unlike Causal’s canvas, Sparkco outputs native Excel with traceable cells.

Automatic formula generation

• What: Creates readable, named-range formulas that spill across time.
• How: Semantic formula engine with time-dimension inference, unit/sign checks, and range vectorization; inserts comments for traceability.
• Benefit: Fewer errors, faster reviews, and easier handoffs.
• Example: IN: ARR churn = opening ARR * monthly churn rate. OUT: =Opening_ARR[t] * Churn_Rate[t] with named ranges and a spilled time series.
• Benchmark: 98.6% internal accuracy on held-out finance tasks; fewer opaque references than typical Excel Copilot or manual formulas.

DCF model builder

• What: Builds FCFF/FCFE DCFs with WACC, terminal value (Gordon or Exit Multiple), and valuation outputs.
• How: Driver synthesis (revenue, margin, capex, NWC), auto WACC from capital structure, XNPV/XIRR, with switches for terminal method.
• Benefit: Investor-grade valuation in minutes with defensible math.
• Example: IN: Build FCFF DCF, 10 years, WACC 10%, g 2.5%. OUT: Sheets: Drivers, Projections, DCF, Valuation with XNPV and sensitivity blocks.
• Benchmark: ~90 seconds to first pass vs hours manually; comparable to Causal’s speed but in native Excel.

Multi-sheet financial model creation (3-statement)

• What: Generates linked Income Statement, Balance Sheet, and Cash Flow with consistency checks.
• How: Accounting-aware linker ensures flows (depreciation, capex, working capital, debt) reconcile; auto roll-forwards and balance checks.
• Benefit: Balanced, audit-ready models without template wrestling.
• Example: IN: Revenue +15% YoY, capex 5% of revenue, straight-line D&A. OUT: 3-statement workbook with drivers and error flags.
• Benchmark: 99% first-pass balance in internal tests; set up in 3–5 minutes vs half-day building in Excel/Anaplan.

Pivot tables and dashboards generation

• What: Auto-builds PivotTables, charts, and slicers from model outputs.
• How: Tags outputs as measures/dimensions, creates pivot caches and linked visuals with clean formatting.
• Benefit: Instant reporting for executives with no manual chart wiring.
• Example: IN: Create a dashboard by region and product margin. OUT: Pivot with slicers (Region, Product) and linked charts.
• Benchmark: Under 30 seconds to first dashboard; compared to DataRails/Anaplan, Sparkco keeps everything in Excel.

Sensitivity analysis

• What: One- and two-way sensitivities and parameter sweeps.
• How: Builds native Excel Data Tables and helper grids tied to named drivers; caches calculations to avoid volatile recalc storms.
• Benefit: Rapid trade-off evaluation with reproducible grids.
• Example: IN: Vary WACC 8–12% and terminal growth 1–4% on Equity Value. OUT: 2D data table heatmap: WACC x g.
• Benchmark: ~80% setup time reduction vs manual Excel; comparable speed to Causal but preserves spreadsheet cells for audit.

Scenario manager

Scenarios are a first-class dimension that flows through every generated formula and sheet.

• What: Define Base/Bull/Bear (or custom) and toggle globally.
• How: Scenario dimension injected via SWITCH/CHOOSE or XLOOKUP keyed to Scenario_Name; writes scenario-specific named ranges and deltas.
• Benefit: Compare cases without duplicating tabs or risking drift.
• Example: IN: Add Pessimistic: growth -20%, hiring pause. OUT: Scenario drop-down with synchronized outputs and notes.
• Benchmark: Single source of truth vs Excel’s legacy Scenario Manager; faster than duplicating model versions.

Named ranges and documentation export

• What: Consistent naming plus an auto-generated data dictionary and README.
• How: Names follow readable patterns (e.g., Revenue_USD[t]); exports glossary (CSV/Markdown) with definitions, units, lineage, and owners.
• Benefit: Faster onboarding and external reviews.
• Example: IN: Export model documentation. OUT: README with Inputs, Drivers, Outputs, and dependency notes.
• Benchmark: Cuts handover time by ~50% vs undocumented spreadsheets; clearer than generic Excel naming conventions.

Model auditing and tracing

• What: End-to-end lineage, checks, and explainability.
• How: Dependency graph surfaces calculation paths; flags circular refs, divide-by-zero, and time misalignments; adds an Audit sheet with checks.
• Benefit: Resolve model issues quickly and build trust with stakeholders.
• Example: IN: Trace EBITDA margin to its drivers. OUT: Stepwise trace showing named inputs and intermediate calculations.
• Benchmark: ~3x faster issue resolution vs manual tracing; more integrated than Excel Inquire for finance-specific checks.

Template library

Sparkco organizes templates by industry, use case, and complexity, with versioning and modular blocks.

• What: Curated, best-practice templates (e.g., SaaS, eCommerce, Manufacturing, FP&A planning, Project finance).
• How: Modular blocks (Revenue, Cohorts, Debt, Capex) with tagged metadata and dependency rules; one-click import overlays onto existing models.
• Benefit: Start from proven patterns and adapt quickly.
• Example: IN: Load SaaS 3-statement with cohort revenue. OUT: Workbook with cohort drivers, retention curves, and dashboards.
• Benchmark: Template spin-up in under 2 minutes; less overhead than Anaplan’s model setup while staying in Excel.

Use Cases and Target Users

Sparkco turns plain-English prompts into auditable Excel workbooks for DCFs and FP&A models, cutting build time from hours-days to minutes while reducing error rates and improving cycle time.

Sparkco is most impactful for teams that repeatedly build, revise, and share financial models under time pressure (FP&A, M&A, corporate finance). It integrates with existing Excel workflows, source systems, and version control to standardize templates, enforce checks, and automate scenario updates.

Benchmarks: building a DCF manually typically takes hours to days; industry studies report high spreadsheet error prevalence and significant time lost to checks. See sources below.

Quantified benefits and KPI improvements

Financial analyst use case

• Pain points: repetitive DCF builds, version drift, late assumption changes.
• Workflow: Prompt: Create a 5-year DCF with revenue CAGR 8%, EBIT margin ramp 12% to 18%, capex 4% of sales, NWC 2% of sales; use WACC 9%, exit multiple 10x; import last 3 years from revenues.csv. Sparkco generates: Assumptions, 3-statement roll-forward, DCF, Sensitivity (WACC, exit), Audit sheet.
• Outcome: Model ready for review in under 30 minutes with traceable formulas and color-coding.
• KPIs: 80–95% cycle-time reduction; error rate reduction 60–80% via automated checks.

FP&A manager use case

• Pain points: monthly reforecasting, manual consolidation, late-breaking inputs.
• Workflow: Prompt: Build model from text for FP&A: quarterly forecast next 8 quarters, driver-based revenues (units x price), OpEx by cost center, headcount roll-forward, scenarios Base/Down 10%. Connect to ERP export fp&a_actuals.xlsx.
• Outcome: One-click refresh and scenario packs; variance bridges auto-built.
• KPIs: Forecast cycle time -85–95%; accuracy +3–5%; error rate -50–80%.

CFO use case

• Pain points: fragmented decks, inconsistent KPIs, slow scenario turnaround.
• Workflow: Prompt: Create board-ready DCF and covenant summary with Base/Upside/Downside and cash runway; pull assumptions from fp&a_master.xlsx; produce slides sheet with charts.
• Outcome: Standardized outputs and audit trail for approvals.
• KPIs: Scenario turnaround time -70–90%; consistency up via shared templates.

M&A analyst use case

• Pain points: bespoke models per target, synergy math errors, tight deadlines.
• Workflow: Prompt: Build merger model: acquirer and target CSVs, DCF for each, pro forma with synergies (revenue 3%, cost 5%), one-time costs $5m, PPA and dilution accretion table.
• Outcome: Review-ready workbook with sensitivity toggles and sources/uses.
• KPIs: Build time -90–97%; linking errors -60–80%; diligence throughput up.

Business owner use case

• Pain points: no modeling staff, needs quick valuation for investors.
• Workflow: Prompt: Quick 5-year DCF using QuickBooks export qb_2022_2024.xlsx, simple drivers (revenue +6%, margin +2 pp), WACC 10%, Gordon Growth 2%.
• Outcome: Clean valuation pack with key charts and assumptions page.
• KPIs: Time -90–95%; clearer assumptions improve stakeholder trust.

Excel power user use case

• Pain points: maintaining bespoke macros, sensitivity grids, and audit checks.
• Workflow: Prompt: Add Monte Carlo sensitivity on WACC (8–12%) and margin (15–20%) to existing model file model_v7.xlsx; output Summary tab with P50/P90 valuation.
• Outcome: Sparkco augments, not replaces, existing workbook; keeps names and styles.
• KPIs: Sensitivity build -80–90%; fewer circular/reference issues.

Data engineer use case

• Pain points: brittle data feeds, manual refresh scripts, version control.
• Workflow: Prompt: Connect Snowflake view FIN.HISTORICALS; schedule weekly refresh; write outputs to SharePoint and tag Git commit with scenario metadata.
• Outcome: Reproducible pipelines and single source of truth.
• KPIs: Manual wrangling -90%; version drift minimized; auditability improved.

Practical workflows

1. Standard DCF from text: Prompt: Create a 5-year DCF with annual revenue, margin assumptions, capex schedule, NWC %, WACC, and exit multiple; import historicals.csv. Output: Assumptions, 3-statement, DCF, Sensitivity, Audit. Time saved: 5–10 hours.
2. Driver-based FP&A model: Prompt: Build quarterly forecast by product with units x price drivers, OpEx by cost center, headcount roll-forward; scenarios Base/Down 10%. Output: Forecast, Variance bridge, Scenario pack. Time saved: 6–12 hours/cycle.
3. M&A synergy DCF: Prompt: Two-company inputs, synergy schedule, PPA, accretion/dilution, sources/uses. Output: Combined model, sensitivities, diligence checklist. Time saved: 9–19 hours.
4. Board package generator: Prompt: Summarize DCF and liquidity runway; export charts and commentary bullets. Output: Slides sheet + PDF. Time saved: 3–6 hours.
5. Model audit/enhancement: Prompt: Scan workbook for inconsistent formulas, hardcodes, circulars; add checks and color coding. Output: Issues log + fixed model. Error reduction: 50–80%.

Technical Specifications and Architecture

Technical specifications for the Excel generator architecture covering system architecture, data flow, security controls, supported Excel formats, scalability, and performance for financial models with strong data security for financial models.

This section details the Excel generator architecture, dataflow, security posture, supported Excel features, and operational characteristics for finance-grade model generation. It references Open XML (ISO/IEC 29500, ECMA-376), serverless deployment patterns, and cryptographic controls aligned to NIST guidance with SOC 2 and ISO 27001 controls.

• Client interfaces: Web UI, CLI, and REST API for synchronous and queued generation.
• NLP and LLM layer: intent parsing, domain templates, and formula synthesis prompts.
• Formula synthesis engine: constraint-checked expression builder producing Excel-compatible formulas and named ranges.
• Workbook generator: streaming XLSX writer compliant with Open XML parts and relationships.
• Storage and versioning: object storage for binaries, metadata in a database, immutable audit trail.
• Security controls: TLS 1.2+/1.3, AES-256 at rest with KMS, role-based access, key rotation, and audit logging.

Architecture and Dataflow

Supported Excel Features and Limits

Architecture Overview

The Excel generator uses a serverless, event-driven design (API Gateway, functions, queues) to scale on demand, isolate tenants, and minimize idle cost. Components are stateless; large artifacts stream to object storage.

• Deployment: AWS Lambda/Azure Functions/Cloud Functions with container images for deterministic builds.
• State: DynamoDB/Cloud Spanner or Cosmos DB for metadata; S3/GCS/Blob Storage for binaries.
• Distribution: CDN with signed URLs for secure downloads.

Security and Compliance

Data in transit uses TLS 1.2+ (pref. TLS 1.3) per NIST SP 800-52r2. Data at rest uses AES-256 with KMS-managed keys; envelope encryption for field-level secrets (NIST SP 800-57). Access is enforced via RBAC, SCIM-provisioned groups, and SSO (SAML 2.0/OIDC).

• Key management: automatic rotation, per-tenant keys on request, HSM-backed.
• Audit: append-only, hash-chained logs with tamper-evidence and retention policies.
• Compliance: SOC 2 Type II and ISO/IEC 27001 alignment; GDPR-ready with DPA and region pinning.
• Data residency: deployable in US, EU, or APAC with region-lock for storage, compute, and logs.

Scalability and Performance

Cold-start optimized runtimes and streaming writers keep latency low for common financial models such as DCF.

Concurrency and throughput can be raised via quota increases; batch jobs are queue-backed for burst absorption.

• Default concurrency: 500 concurrent jobs/region; burst to 2,000 with pooling.
• File size: up to 100 MB .xlsx (zipped); soft limit adjustable with streaming mode.
• Expected DCF generation: P50 3–8 s, P95 10–15 s for 5-year, 5–7 sheets, ~15k formulas.
• Throughput: ~60–120 workbooks/min/region under typical limits.

SLAs and Operations

Service targets reflect finance-grade reliability while maintaining flexibility for complex models.

• Availability: 99.9% monthly SLA (excludes scheduled maintenance).
• RTO/RPO: RTO 4 hours, RPO 1 hour for control plane; 0 data loss for completed artifacts.
• Support: first response within 1 business day (standard) or 2 hours (premium).
• Job success SLO: 99.5% excluding user errors (invalid formulas/data).

Integration Points

The system integrates with external data sources and identity providers to automate financial model inputs.

• Data sources: REST/GraphQL, S3/GCS/Blob presigned pulls, JDBC wrappers (Snowflake, BigQuery, Redshift).
• Secrets: KMS/Key Vault/Cloud KMS with per-connector credentials.
• Webhooks: job status callbacks, artifact ready events.
• ETL: optional ingestion via scheduled connectors or event-driven triggers.

Sensitive Input Handling

Inputs are minimized and redacted before the LLM stage; secrets never enter prompts. A zero-retention mode deletes payloads and intermediates after generation. Customer data is excluded from training and shared models.

Limitations and Mitigations

• Macros: .xlsm not produced; mitigation: use approved add-ins or Power Query steps for automation.
• Volatile functions: limited to protect performance; mitigation: replace with structured references.
• Very large pivots/charts: cache size capped; mitigation: pre-aggregate upstream.
• Legacy Excel (<2007) compatibility: not guaranteed; mitigation: provide .xls export via converter if needed.
• Ultra-large models (>100 MB): require streaming mode and partitioned sheets.

Standards and References

Open XML: ISO/IEC 29500, ECMA-376, Microsoft Open Specifications for SpreadsheetML.

Serverless: AWS Well-Architected Framework Serverless Lens, Azure Serverless documentation, Google Cloud Functions/Run best practices.

Crypto: NIST SP 800-52r2 (TLS), NIST SP 800-57 Pt.1 (Key Management).

Compliance: AICPA SOC 2 Trust Services Criteria, ISO/IEC 27001 Annex A controls.

Integration Ecosystem and APIs

Sparkco plugs into your finance stack with ready-made integrations and a text to Excel API. Use our API for Excel generator, SDKs, and webhooks to automate secure, compliant workflows across FP&A data sources.

Built for FP&A teams, Sparkco connects to common finance data sources (S3, Google Drive, OneDrive, SQL) and publishes outputs to BI tools and ERPs. Developers get OAuth2 and API key auth, clear rate limits, and predictable webhooks to wire Sparkco into existing pipelines.

Connectors

Use built-in connectors to read source data and deliver output workbooks. Common FP&A sources include ERP extracts, data warehouses, and cloud drives. Pre-built destinations simplify getting results into BI and planning tools.

• Supported sources: CSV (upload or URL), Amazon S3, Google Drive, OneDrive/SharePoint, SQL databases (PostgreSQL, MySQL, SQL Server, Snowflake).
• Pre-built outputs: Power BI, Tableau, and ERP handoffs (NetSuite, SAP, Oracle) via S3/Drive folders or HTTPS file endpoints.
• Patterns: schedule syncs, event-driven refresh on file drop, or on-demand via REST API.

Connector matrix

REST API

Base URL: https://api.sparkco.com. Auth: OAuth2 (Client Credentials or Authorization Code with PKCE) or API key. Send Authorization: Bearer TOKEN or x-api-key: KEY. Scopes limit access (e.g., files.read, jobs.write).

Endpoints at a glance:

POST /v1/generate — create a workbook job

GET /v1/jobs/{jobId} — job status and result

POST /v1/batches — batch generation

GET /v1/batches/{batchId} — batch status

POST /v1/validate — programmatic formula checks

POST /v1/webhooks — register callback endpoints

Example request payloads

SDKs

Python SDK (sparkco) and JavaScript SDK (@sparkco/sdk) wrap auth, retries, and pagination.

Python pseudocode: client = Sparkco(client_id, client_secret) job = client.generate(prompt="Budget from S3", data_sources=[{"type":"s3","uri":"s3://bucket/file.csv"}]) result = client.jobs.wait(job.id) client.files.download(result.file_url, "budget.xlsx")

JavaScript pseudocode: const client = new Sparkco({ apiKey: process.env.SPARKCO_KEY }) const job = await client.generate({ prompt: "Variance analysis", output: { format: "xlsx" } }) const res = await client.jobs.wait(job.id) await client.files.download(res.fileUrl, "variance.xlsx")

Webhooks

Register webhooks at POST /v1/webhooks with events: job.completed, job.failed, batch.completed, file.available. Each event includes HMAC-SHA256 signature in X-Sparkco-Signature using your shared secret.

Webhook example payload: { "type": "job.completed", "id": "evt_abc123", "data": { "jobId": "job_789", "status": "succeeded", "fileUrl": "https://files.sparkco.com/wb/job_789.xlsx", "metadata": {"source": "s3://fpna/acme/history.csv"} } }

• Retry on 5xx with exponential backoff.
• Validate timestamps and signature before processing.
• Return 2xx to acknowledge; otherwise we retry with backoff for 72 hours.

Automation examples

1) Submit a plain-English prompt and receive an XLSX: Request: POST /v1/generate with { "prompt": "Cash flow model from last 8 quarters", "dataSources": [{"type":"drive","id":"1Abc"}], "output": {"format":"xlsx"} } Response: 202 { "jobId": "job_123" } Poll: GET /v1/jobs/job_123 -> { "status": "succeeded", "fileUrl": "https://.../job_123.xlsx" }

2) Run batch generation for a portfolio: Request: POST /v1/batches with { "items": [{"id":"AAPL","prompt":"Plan"},{"id":"MSFT","prompt":"Plan"}], "delivery": {"s3": "s3://fpna/portfolio/q4/"} } Webhook: batch.completed -> contains per-item results and locations

3) Validate generated formulas programmatically: Request: POST /v1/validate with { "workbook": {"url": "https://.../job_123.xlsx"}, "checks": ["circular-refs","volatile-functions","hardcodes-in-formulas"] } Response: { "issues": [{"sheet":"P&L","cell":"D42","severity":"warning","rule":"hardcodes-in-formulas","message":"Literal 0.25 in formula"}] }

Integration with BI: publish XLSX and CSV outputs to S3 or OneDrive folders monitored by Power BI/Tableau, or expose signed HTTPS URLs as data sources for scheduled refresh.

Pricing Structure and Plans

Transparent subscription pricing for our AI Excel generator: clear tiers, fair-usage quotas, optional add-ons, and enterprise licensing.

Our pricing uses tiered, per-user subscription with pooled usage quotas for AI generations and API calls. Choose a plan by team size and control needs, then add enterprise-grade options as required.

No hidden fees. Set hard caps or allow pay-as-you-go overages with clear rates. Trials mirror real usage so you can evaluate integrations, templates, and collaboration before subscribing.

Plans at a Glance

How to choose a plan

• Solo analyst: Starter for basics and core templates; Professional if you need API access, larger quotas, or SSO.
• FP&A team (3–15 users): Team for collaboration, SCIM, and higher limits.
• Enterprise finance org (25+ users): Enterprise for security reviews, audit logging, private cloud/on‑prem, and dedicated support.

Add-ons and usage

• On‑prem/private cloud deployment: from $2,000/month platform fee + $50/user + $10,000 one-time setup.
• Dedicated 99.9% SLA: $500/month per account (included in Enterprise).
• Premium template packs: $29/user/month.
• Advanced compliance (data retention/legal hold): $300/month.
• Burst capacity: additional usage at posted overage rates with spend caps.

Trial, billing, and licensing FAQ

• What is included in the trial for AI Excel generator? 14 days of Professional features, 2 users, 2,000 AI generations, 20,000 API calls, no credit card required.
• Are there overage fees? Yes, usage above plan quotas is billed at the posted rates; you can set hard caps.
• Billing terms: monthly or annual (15% discount). Upgrades prorated immediately; downgrades at renewal.
• Cancellation: cancel anytime; access remains through the paid term with one-click export.
• Licensing: named-user seats; view-only guests are free on Team and Enterprise.
• Data residency: US/EU regions on Team and Enterprise; on‑prem available as an add-on.

Enterprise procurement steps

1. Security review: SOC 2 Type II, pen test summary, DPA, data-flow and architecture docs.
2. Commercials: MSA + Order Form with volume/term discounts and usage commitments if needed.
3. IT setup: SSO/SCIM, audit logging, data residency, integrations (ERP/HRIS/CRM/warehouse).
4. Implementation: optional SOW for onboarding and change management; CSM assigned.
5. Payment: PO accepted, Net 30 by ACH/wire; invoicing via annual or quarterly schedules.

Implementation and Onboarding

Authoritative 30/60/90 onboarding and implementation plan for finance teams, including presale POC guidance, technical prerequisites, a measurable pilot, training curriculum, and governance. Designed to help FP&A deliver a production-ready DCF fast, with clear success metrics and rollout checklists for IT and finance stakeholders.

Day 0–30: Foundation and POC

• Name executive sponsor, FP&A lead, project manager; define scope, use cases, success metrics, and go/no-go criteria.
• Run presale POC (10 business days): load 24–36 months of GL, revenue, and headcount; build a mini 3-statement and DCF slice; validate data mapping and outputs.
• Technical readiness: enable SSO (SAML/OIDC) and SCIM; connect ERP/CRM/billing/data warehouse; set up SFTP CSV fallback.
• Establish roles and RBAC; implement least-privilege, audit logs, approval flows, and workspace structure.
• Capture baselines: model build time, forecast cycle time, error count, weekly active users, stakeholder CSAT.
• Training (mandatory): Orientation and navigation (self-serve), Data integration and mapping (live workshop).
• Artifacts: chart of accounts mapping, dimension dictionary, assumptions register, data refresh schedule.

Day 31–60: Pilot execution and expansion

• Launch pilot cohort (6–12 users) across FP&A and a business unit; define sprint cadence and demo schedule.
• Build first production-ready DCF for one business unit by Day 45 (10–15 business days after mapping).
• Enable data quality routines: reconciliation to GL, variance checks, exception alerts, scheduled refreshes.
• Adopt templates: 3-statement model, revenue cohort, driver-based Opex, headcount planning.
• Track KPIs weekly vs baseline; collect feedback; tune permissions, workflows, and templates.
• Training (mandatory for modelers): Modeling and DCF build (live + lab). Optional: Reporting and variance (self-serve).

Day 61–90: Production and scale

• Extend to company-level forecast and scenario library; automate monthly reporting packs and distribution.
• Run mock close and forecast cycle; secure sign-offs from Controller, FP&A lead, and business partners.
• Harden governance: change control, versioning, promotion path from sandbox to production, lineage/audit.
• Finalize pilot report: outcomes vs targets, adoption and quality metrics, cost/performance review, go/no-go.
• Training: Governance and controls (admins), Office hours for advanced modeling; certify authors before write access.

Presale POC guidance

• Timebox to 2 weeks with clear decision gates and executive sponsor engagement.
• Scope: 1–2 priority use cases (e.g., BU DCF, Opex forecast); success = measurable reductions in build time and errors, validated tie-outs.
• Data required: 24–36 months GL and sub-ledger detail, revenue cohorts/CRM, headcount/comp, key assumptions (WACC, tax, growth).
• Resources (customer): executive sponsor (0.1 FTE), FP&A lead (0.3), 1–2 analysts (0.5), IT data engineer (0.2), security reviewer (as needed).
• Deliverables: validated data model, mapped dimensions, draft DCF, KPI baseline, risks/assumptions log, next-step plan.

Technical prerequisites

• Identity: SAML/OIDC SSO and SCIM provisioning; MFA enforced for admins.
• Data: ERP (NetSuite/SAP/Oracle/Dynamics), CRM (Salesforce), billing, HRIS; DWH (Snowflake/BigQuery/Redshift); SFTP CSV fallback.
• Security: SOC 2 reports available, encryption in transit/at rest, DLP options, IP allowlist, audit logs exportable.
• Environment: modern browser, network egress to required endpoints, service accounts for pipelines.
• Admin setup: RBAC roles, workspace projects, approval flows, data retention and backup policies.

Sample pilot plan

• Objectives: 1) Cut model build time and forecast cycle time, 2) Improve accuracy and reduce errors, 3) Increase adoption and collaboration, 4) Deliver a production-ready BU DCF.
• Example prompts for analysts:
• - Build a DCF using 24 months of historical cash flows, WACC 9%, terminal growth 2%; include sensitivity at +/-100 bps.
• - Explain last month’s Opex variance by department vs budget and prior year; list top 5 drivers in $ and %.
• - Create a scenario with churn +2 pts and new ARR -10%; update 3-statement model and cash runway.
• - Generate a driver-based headcount plan for Engineering with comp bands and hiring ramp; summarize quarterly cost impact.
• - Produce a board-ready summary of revenue, margin, cash, and key variances with charts.

Pilot success metrics

Training curriculum

• Mandatory before write access: Orientation and navigation, Data integration and mapping, Modeling and DCF build, Governance and controls. Optional: Reporting and variance, Templates gallery, Office hours.

Modules, formats, and audiences

Governance recommendations

Estimated timeline and DCF readiness

• Typical FP&A pilot: 4–6 weeks end-to-end with dedicated resources.
• Production-ready DCF: 10–20 business days after data mapping and mandatory training, assuming clean historicals and an assigned modeler.

FP&A pilot timeline

Customer-side resources

Customer Success Stories and Case Studies

Three concise, results-driven case studies show how Sparkco accelerates DCFs and financial models with measurable impact. Each case study follows a Problem, Solution, Results, and Quote structure and includes the exact prompt used.

See how finance teams build audit-ready DCFs and driver-based models in minutes, not days. Each case study includes a plain-English prompt, concrete outputs, and quantified business outcomes.

Timeline of Key Events and Outcomes

DCF Case Study: Anonymized Investment Bank (M&A Due Diligence)

Customer profile: Mid-market investment bank, 120 employees; M&A analysts and associates.

• Problem: Building DCFs and comps from CIMs/data rooms took 10–16 hours per target and introduced formula risk.
• Solution: Sparkco ingested CIM PDFs and trial balances from Office 365/SharePoint; auto-built a 3-statement model, DCF, comps, and a football field with scenario toggles.
• Prompt used: Build a buy-side DCF and trading comps for TargetCo using CIM_v3.pdf and TB_Q4.csv. Assume WACC 9–10%, tax 25%. Provide base, upside, downside, and a football field chart.
• Generated workbook: 9-sheet Excel (Assumptions; IS/BS/CF; Segments; DCF; Comps; Sensitivity; Sources & Uses; Summary) with linked disclosures and audit trail.
• Results: Time-to-first-valuation cut from 16h to 45m (95% faster); 24h earlier bid; zero blocker errors in QA; enabled same-day IC greenlight.
• Quote: We moved from days to minutes. Sparkco let us test valuation angles and submit a higher-confidence bid ahead of rivals. — VP, M&A (anonymized)

DCF Case Study: Anonymized B2B SaaS (FP&A Forecasting)

Customer profile: B2B SaaS, 300 employees; CFO and FP&A team.

• Problem: Rolling forecast rebuilds consumed 5 days each month; scenarios and board refreshes were slow and inconsistent.
• Solution: Sparkco connected to NetSuite, Salesforce, and Snowflake; produced a driver-based 3-statement model with ARR waterfall, cohort churn/expansion, CAC payback, and cash runway.
• Prompt used: Create a monthly 3-statement, driver-based SaaS model with ARR waterfall, churn/expansion cohorts, CAC payback, and runway. Include flat/target/stretch scenarios and sync to NetSuite GL and Salesforce ARR.
• Generated workbook: Consolidated model with scenario manager, variance bridges, rolling 24-month outlook, and board-ready charts.
• Results: Forecast cycle cut from 5 days to 2 hours (96% faster); forecast MAPE improved 18%; month-end close shortened by 1 day; enabled timely hiring and GTM spend decisions.
• Quote: Sparkco turned our forecast into a living model. We scenario-plan in minutes and hit the board with answers, not placeholders. — CFO (anonymized)

DCF Case Study: Anonymized PE Fund ($2.5B AUM) Portfolio Valuations

Customer profile: Private equity fund, 45 employees; valuation and portfolio ops team across 12 companies.

• Problem: Quarterly marks required inconsistent templates and 30+ hours per company; audit cycles dragged and distracted deal teams.
• Solution: Sparkco standardized a valuation template and synced KPIs from Snowflake and Box; produced DCFs, trading comps, and sensitivity packs with an audit log.
• Prompt used: Standardize Q1 valuation models for 12 portfolio companies using prior-quarter templates and latest KPIs in Snowflake. Build DCF and trading comps, apply sector WACC ranges, and export an auditor-ready pack.
• Generated workbook: Master template plus 12 company files with sector assumptions, SIC-mapped comps, unit economics, and versioned audit trails.
• Results: Prep time reduced from 30h to 6h per company (80% faster); audit review comments down 60%; delivered one week earlier; enabled top-down macro shock testing in under 30 minutes.
• Quote: We finally have consistent, defensible marks. Auditors loved the traceability, and our team got a week back. — Head of Portfolio Ops (anonymized)

Get the PDF

Download the full PDF case study pack with prompts, templates, and checklists: sparkco.com/case-studies.pdf

Support, Documentation, and Community

Central hub for support resources, documentation, API reference, and the prompt library for text-to-spreadsheet workflows. Find sample prompts, training, community, and enterprise SLAs, plus best practices inspired by Stripe and Twilio.

Support Channels and SLAs

Choose the channel that matches your urgency and plan. Enterprise customers receive 24/7 priority handling and tracked SLAs.

Support Channels

Enterprise SLAs

Documentation Structure Template

Docs are written and versioned alongside code (docs-as-code) and auto-updated at release. Follow this article template for consistent, searchable documentation.

• Problem statement and audience
• Prerequisites and sample input (prompt text, CSV or API payload)
• Expected workbook output (sheets created, key cells, formulas, charts)
• Step-by-step instructions with annotated screenshots
• Troubleshooting: common errors and fixes
• Links to relevant API reference (endpoints, webhooks, errors)
• Version, last updated date, change history
• Tags and search keywords; related articles

• OpenAPI-sourced SDK snippets with language toggles
• Doc tests validate prompt-to-worksheet outputs in CI
• Changelog and versioned docs keep content aligned with product updates
• Error catalog and status codes mapped to remediation steps

API Reference Best Practices

Modeled after Stripe and Twilio: fast to scan, task-oriented, and testable.

• Interactive console with copyable curl and SDK snippets
• Language toggles and example requests/responses
• Error directory with causes, retryability, and fixes
• Webhook event samples with payload schemas
• Pagination, filtering, rate limits, idempotency guidelines
• Sandbox credentials and test vectors
• Release notes linking to changed endpoints and migration guides

Text-to-Spreadsheet Workflow Docs

Structure guides around outcomes: define the goal, map prompts to spreadsheet schema, and show validation.

• Business goal and success metrics
• Prompt-to-sheet schema mapping (sheets, ranges, data types)
• Validation rules (constraints, formula checks)
• Data sources and permissions required
• Post-processing (formulas, pivots, charts)
• QA steps: diff generated vs expected workbook
• Export, sharing, and governance recommendations

Prompt Library

Access curated prompts by model type with tags, versioning, and user ratings. Clone, customize, and pin favorites to teams.

• Organization: by model type (DCF, three-statement, dashboard), industry, complexity, connectors
• Versioning: semantic versions, deprecation notices, changelogs
• Quality signals: user ratings, usage count, last verified date
• Metadata: required inputs, expected outputs, API endpoints used

Prompt Library Index

Community and Training Resources

Learn by watching, reading, and collaborating. Everything is searchable and linked from the Help menu.

Resource Tiles

Security, Privacy, and Data Governance

Sparkco delivers finance-grade security with SOC 2 controls, ISO-aligned data governance, strong encryption, fine-grained access, and a complete audit trail. Customers control data residency and can validate our posture during procurement.

Our platform is engineered for regulated finance teams: secure by default, auditable end to end, and configurable to your data residency, retention, and approval needs.

Security Controls

Defense-in-depth safeguards financial models, generated files, and collaboration data with layered encryption, least-privilege access, and immutable audit trails.

• Encryption: TLS 1.2+ in transit with perfect forward secrecy; AES-256 at rest; FIPS 140-2 validated modules; envelope encryption with HSM-backed keys; automated rotation.
• Key management: Provider KMS or dedicated HSM; BYOK/HYOK; split roles so no single operator can decrypt data.
• Access controls: SSO (SAML/OIDC), MFA, RBAC with scoped roles and optional ABAC, SCIM provisioning, just-in-time privileged access with approvals.
• Data handling: Tenant isolation, production/non-production separation, secrets manager, DLP for uploads, optional field-level encryption for PII/financial identifiers.
• Audit trail: Tamper-evident logs for user/admin actions, API calls, data access, model changes; signed, time-synchronized; export to SIEM (syslog/webhook).
• Model lineage and approvals: Versioned model registry, dataset and configuration hashes, signed artifacts, 4-eyes approval workflow, segregation of duties, rollback.
• Retention and deletion: Policy-driven retention (e.g., 1–7 years), legal holds, time-bounded backups (encrypted), verified deletion with 30-day SLA.
• Generated files: Encrypted object storage, malware scanning, time-limited signed URLs, checksum integrity, optional watermarking and object-level access policies.
• Excel best practices: Store in managed SharePoint/OneDrive with IRM, enforce strong passwords, disable unsigned macros by default, sign templates, template whitelisting, and version control.

Compliance and Validation

Our controls map to finance expectations for security, availability, confidentiality, and privacy. Evidence packages are available during diligence.

1. Request SOC 2 Type II report, ISO 27001 certificate and SOA, and most-recent penetration test with remediation status.
2. Review data flow diagrams, subprocessor list, and regional data residency mapping; confirm DPA, SCCs, and RoPA.
3. Validate RBAC roles and least-privilege defaults; test SSO/MFA and SCIM in a sandbox.
4. Evaluate audit log exports to your SIEM and sample event evidence for a mock incident.
5. Confirm BYOK/HYOK setup, key rotation policy, and access reviews; verify key-use logs.
6. Assess backup, DR (RPO/RTO), and change management for model approvals and rollbacks.

Compliance Posture

Data Residency and Deployment

Choose data location (US, EU, APAC) with all storage, backups, and logs confined to region. Private networking, IP allowlists, and VPC peering are available. For strict controls, deploy in a dedicated VPC or on-prem with customer-managed keys.

FAQ

• How are sensitive financial inputs protected? Inputs are encrypted in transit and at rest with AES-256. Optional field-level encryption and in-memory redaction minimize exposure. Customer data is not used to train shared models by default.
• How are audit logs presented? A searchable UI plus export to CSV/JSON and streaming to your SIEM. Events include who, what, when, where (IP/device), before/after for configs, and model lineage; retention follows your policy.
• How does versioning help governance? Every dataset, notebook, and model is immutably versioned with signatures and hashes. Approvals are tied to versions, enabling traceability, reproducible results, and controlled rollback during audits.

Competitive Comparison Matrix

Sparkco vs competitors: direct differentiation and potential weaknesses

Buyer guidance: when to choose Sparkco vs alternatives

Pricing transparency snapshot

Sourcing footnotes